Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
| enable f16; | |
| @group(0) @binding(0) | |
| var<storage, read_write> input: array<SRC_TYPE>; | |
| @group(0) @binding(1) | |
| var<storage, read_write> output: array<DST_TYPE>; | |
| struct Params { | |
| offset_i: u32, | |
| offset_o: u32, | |
| // element strides | |
| si0: u32, si1: u32, si2: u32, si3: u32, | |
| so0: u32, so1: u32, so2: u32, so3: u32, | |
| src_w: u32, | |
| src_h: u32, | |
| src_z: u32, | |
| src_n: u32, | |
| dst_w: u32, | |
| dst_h: u32, | |
| dst_z: u32, | |
| dst_n: u32, | |
| mode_flags: u32, | |
| }; | |
| @group(0) @binding(2) | |
| var<uniform> params: Params; | |
| const GGML_SCALE_FLAG_ALIGN_CORNERS: u32 = 1u << 8u; | |
| fn get_clamped_input(x: i32, y: i32, z: u32, n: u32) -> f32 { | |
| let cx = u32(clamp(x, 0, i32(params.src_w) - 1)); | |
| let cy = u32(clamp(y, 0, i32(params.src_h) - 1)); | |
| let i = params.offset_i + cx * params.si0 + cy * params.si1 + z * params.si2 + n * params.si3; | |
| return f32(input[i]); | |
| } | |
| fn cubic_weight(t: f32, a: f32) -> f32 { | |
| let at = abs(t); | |
| if (at <= 1.0) { | |
| return (a + 2.0) * at * at * at - (a + 3.0) * at * at + 1.0; | |
| } else if (at <= 2.0) { | |
| return a * at * at * at - 5.0 * a * at * at + 8.0 * a * at - 4.0 * a; | |
| } else { | |
| return 0.0; | |
| } | |
| } | |
| @compute @workgroup_size(WG_SIZE) | |
| fn main( | |
| @builtin(global_invocation_id) gid: vec3<u32>, | |
| @builtin(num_workgroups) num_wg: vec3<u32> | |
| ) { | |
| let i_out = gid.x + (num_wg.x * u32(WG_SIZE)) * gid.y; | |
| let total = params.dst_w * params.dst_h * params.dst_z * params.dst_n; | |
| if (i_out >= total) { | |
| return; | |
| } | |
| // decode (x, y, z, n) | |
| var i = i_out; | |
| let x_dst = i % params.dst_w; | |
| i = i / params.dst_w; | |
| let y_dst = i % params.dst_h; | |
| i = i / params.dst_h; | |
| let z_dst = i % params.dst_z; | |
| let n_dst = i / params.dst_z; | |
| // scale factors | |
| var sf0 = f32(params.dst_w) / f32(params.src_w); | |
| var sf1 = f32(params.dst_h) / f32(params.src_h); | |
| var sf2 = f32(params.dst_z) / f32(params.src_z); | |
| var sf3 = f32(params.dst_n) / f32(params.src_n); | |
| let align_corners = (params.mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) != 0; | |
| // pixel_offset: 0.5 for half-pixel-center (default), 0.0 for align_corners | |
| var pixel_offset = 0.5; | |
| if (align_corners) { | |
| pixel_offset = 0.0; | |
| if (params.dst_w > 1 && params.src_w > 1) { | |
| sf0 = f32(params.dst_w - 1) / f32(params.src_w - 1); | |
| } | |
| if (params.dst_h > 1 && params.src_h > 1) { | |
| sf1 = f32(params.dst_h - 1) / f32(params.src_h - 1); | |
| } | |
| } | |
| let z_src = min(params.src_z - 1, u32(floor(f32(z_dst) / sf2))); | |
| let n_src = min(params.src_n - 1, u32(floor(f32(n_dst) / sf3))); | |
| var result = 0.0; | |
| let x_src = min(params.src_w - 1, u32(floor(f32(x_dst) / sf0))); | |
| let y_src = min(params.src_h - 1, u32(floor(f32(y_dst) / sf1))); | |
| result = get_clamped_input(i32(x_src), i32(y_src), z_src, n_src); | |
| // Antialiased bilinear: triangle filter over a variable support region. | |
| let support0 = max(1.0f / sf0, 1.0f); | |
| let support1 = max(1.0f / sf1, 1.0f); | |
| let invscale0 = 1.0 / support0; | |
| let invscale1 = 1.0 / support1; | |
| let fx = (f32(x_dst) + pixel_offset) / sf0; | |
| let fy = (f32(y_dst) + pixel_offset) / sf1; | |
| let x_min = max(i32(fx - support0 + pixel_offset), 0); | |
| let y_min = max(i32(fy - support1 + pixel_offset), 0); | |
| let x_max = min(i32(fx + support0 + pixel_offset), i32(params.src_w)); | |
| let y_max = min(i32(fy + support1 + pixel_offset), i32(params.src_h)); | |
| var weighted_sum = 0.0; | |
| var total_weight = 0.0; | |
| for (var x = x_min; x < x_max; x += 1) { | |
| let wx = max(1.0 - abs(f32(x) - fx + pixel_offset) * invscale0, 0.0); | |
| for (var y = y_min; y < y_max; y += 1) { | |
| let wy = max(1.0 - abs(f32(y) - fy + pixel_offset) * invscale1, 0.0); | |
| let w = wx * wy; | |
| if (w > 0.0) { | |
| weighted_sum += get_clamped_input(x, y, z_src, n_src) * w; | |
| total_weight += w; | |
| } | |
| } | |
| } | |
| if (total_weight > 0.0) { | |
| result = weighted_sum / total_weight; | |
| } | |
| let fx = (f32(x_dst) + pixel_offset) / sf0 - pixel_offset; | |
| let fy = (f32(y_dst) + pixel_offset) / sf1 - pixel_offset; | |
| let x0 = i32(floor(fx)); | |
| let y0 = i32(floor(fy)); | |
| let dx = clamp(fx - f32(x0), 0.0, 1.0); | |
| let dy = clamp(fy - f32(y0), 0.0, 1.0); | |
| let a = get_clamped_input(x0, y0, z_src, n_src); | |
| let b = get_clamped_input(x0 + 1, y0, z_src, n_src); | |
| let c = get_clamped_input(x0, y0 + 1, z_src, n_src); | |
| let d = get_clamped_input(x0 + 1, y0 + 1, z_src, n_src); | |
| let wa = (1.0 - dx) * (1.0 - dy); | |
| let wb = dx * (1.0 - dy); | |
| let wc = (1.0 - dx) * dy; | |
| let wd = dx * dy; | |
| result = a * wa + b * wb + c * wc + d * wd; | |
| // bicubic convolution with alpha = -0.75 (PyTorch default) | |
| let alpha = -0.75; | |
| let fx = (f32(x_dst) + pixel_offset) / sf0 - pixel_offset; | |
| let fy = (f32(y_dst) + pixel_offset) / sf1 - pixel_offset; | |
| let x0 = i32(floor(fx)); | |
| let y0 = i32(floor(fy)); | |
| let dx = fx - f32(x0); | |
| let dy = fy - f32(y0); | |
| // horizontal weights for offsets -1, 0, 1, 2 | |
| let wx0 = cubic_weight(dx + 1.0, alpha); | |
| let wx1 = cubic_weight(dx, alpha); | |
| let wx2 = cubic_weight(1.0 - dx, alpha); | |
| let wx3 = cubic_weight(2.0 - dx, alpha); | |
| // vertical weights for offsets -1, 0, 1, 2 | |
| let wy0 = cubic_weight(dy + 1.0, alpha); | |
| let wy1 = cubic_weight(dy, alpha); | |
| let wy2 = cubic_weight(1.0 - dy, alpha); | |
| let wy3 = cubic_weight(2.0 - dy, alpha); | |
| // intermediate horizontal interpolation for 4x4 grid of pixels | |
| // x0-1, x0, x0+1, x0+2, y0-1 | |
| let p0 = get_clamped_input(x0 - 1, y0 - 1, z_src, n_src); | |
| let p1 = get_clamped_input(x0, y0 - 1, z_src, n_src); | |
| let p2 = get_clamped_input(x0 + 1, y0 - 1, z_src, n_src); | |
| let p3 = get_clamped_input(x0 + 2, y0 - 1, z_src, n_src); | |
| let row0 = p0 * wx0 + p1 * wx1 + p2 * wx2 + p3 * wx3; | |
| // x0-1, x0, x0+1, x0+2, y0 | |
| let q0 = get_clamped_input(x0 - 1, y0, z_src, n_src); | |
| let q1 = get_clamped_input(x0, y0, z_src, n_src); | |
| let q2 = get_clamped_input(x0 + 1, y0, z_src, n_src); | |
| let q3 = get_clamped_input(x0 + 2, y0, z_src, n_src); | |
| let row1 = q0 * wx0 + q1 * wx1 + q2 * wx2 + q3 * wx3; | |
| // x0-1, x0, x0+1, x0+2, y0+1 | |
| let r0 = get_clamped_input(x0 - 1, y0 + 1, z_src, n_src); | |
| let r1 = get_clamped_input(x0, y0 + 1, z_src, n_src); | |
| let r2 = get_clamped_input(x0 + 1, y0 + 1, z_src, n_src); | |
| let r3 = get_clamped_input(x0 + 2, y0 + 1, z_src, n_src); | |
| let row2 = r0 * wx0 + r1 * wx1 + r2 * wx2 + r3 * wx3; | |
| // x0-1, x0, x0+1, x0+2, y0+2 | |
| let s0 = get_clamped_input(x0 - 1, y0 + 2, z_src, n_src); | |
| let s1 = get_clamped_input(x0, y0 + 2, z_src, n_src); | |
| let s2 = get_clamped_input(x0 + 1, y0 + 2, z_src, n_src); | |
| let s3 = get_clamped_input(x0 + 2, y0 + 2, z_src, n_src); | |
| let row3 = s0 * wx0 + s1 * wx1 + s2 * wx2 + s3 * wx3; | |
| // final vertical interpolation | |
| result = row0 * wy0 + row1 * wy1 + row2 * wy2 + row3 * wy3; | |
| let dst_idx = params.offset_o + x_dst * params.so0 + y_dst * params.so1 + z_dst * params.so2 + n_dst * params.so3; | |
| output[dst_idx] = DST_TYPE(result); | |
| } | |